Breast cancer is one of the leading causes of death in women in developed countries. The most effective way to improve prognosis and survival rate is early detection and treatment of breast cancer. Currently, mammography is the most effective method for early diagnosis of breast cancers. Studies have shown that radiologists fail to detect cancer that was evident retrospectively on radiographs. The missed detections may be due to the subtle nature of the radiographic findings, poor image quality, eye fatigue or simply oversight by radiologists. It is has been suggested that the reading of mammograms by two radiologists increases cancer detection rate by 15%. As an alternative method to double reading, computerized methods have been investigated as an aid to radiologists in improving their diagnostic accuracy. Computer-aided diagnosis (CAD) is defined as a diagnosis made by a radiologist who uses the output of a computer analysis of an image.
Various systems and methods are currently known as CAD systems for computerized detection of abnormalities on radiological images, such as those disclosed by Giger et al. in “Handbook of Medical Imaging” published by SPIE Press. It has been shown that use of CAD systems improves radiologists' sensitivity in detecting cancer with a slight increase in recall rate caused by the false-positive markers generated by the computer. Currently, the suspicious locations identified by computerized detection algorithm are typically annotated by arrows, circles or other symbols and numerical numbers on a display medium to direct radiologists' attention to these detected regions-of-interest (ROIs), such as those disclosed by patents WO 0242998 and WO 02002056240.
For exams using screen/film systems, a radiologist typically uses a light box to analyze images. For images acquired digitally, for example, computed radiography and digital radiography systems for breast and chest imaging, image interpretation using a workstation equipped with a high-resolution display has become a trend. The advantages of digital capture and display over screen/film system include the wide dynamic range and the ability to manually adjust the image to have the look based on an individual preference for a better diagnosis.
One of the limitations that prevent CAD from being widely used in clinical practice is the low tolerance for the number of false-positive markers per image generated by a CAD algorithm. Radiologists may feel compelled to reexamine the computer-detected regions to make follow-up recommendation necessary. Manual manipulation of images to obtain an optimal display of each computer-identified ROI may be performed by individual radiologists. In clinical environment where high volume x-ray screening procedures are performed routinely and the majority of the exams are negative, a large number of false-positive detections could be a major factor that slows down the reading process.
Additional information such as the computer-estimated probability from the above methods could potentially help radiologists in their decision making process. For example, radiologists may spend less time on computer-detected regions with a low probability of being cancer. However, radiologists may still likely to exam some CAD detections of low probabilities with manually manipulation of the images to optimize the look for a better diagnosis of these CAD detected abnormalities
U.S. patent application No. 2005/0240882 (Morita et al.) is directed to a method for displaying a number of computer-detected regions of pathological interest of an anatomic feature, comprising steps of displaying an image of the anatomical feature, simultaneously displaying with the image a uniquely identified marker corresponding to each computer-detected region of pathological interest; wherein each marker is generated from the image by a computer-implemented detection algorithm and is configured to incorporate viewable classification data entered by a user. Each marker is configured to visually indicate the probability of cancer determined by the computer-implemented detection algorithm, where in the color of each marker visually indicate the probability of cancer determined by computer-implemented algorithm.
WO Application No. 02/42998 (Roehrig et al.) is directed to a method providing annotation information that can include an assessment of the probability, likelihood or predictive of the CAD-detected suspicious abnormalities as an additional aid to radiologists.
U.S. patent application No. 2002/0181797 (Yang et al.) is directed to a method improving disease diagnosis using contrast enhancement weighted for different frequency contents in mammography.
U.S. patent application No. 2002/0154802 (Goldkuhl et al.) is directed to a method for mammography contrast enhancement using multiple exposure technique.
U.S. patent application No. 2003/0091222 (Yang et al.) is directed a method improving global contract enhancement for digital portal images.
U.S. Pat. No. 6,778,691 (Barski et al.) is directed to a method improving global contrast for general chest radiography.
U.S. Ser. No. 11/549,130 (Huo et al), titled METHOD FOR ENHANCED VISUALIZATION OF MEDICAL IMAGES, commonly assigned, is directed to a method improving the visibility of microcalcifications while improving the overall contrast of digital mammograms.
U.S. patent application No. 2005/0135695 (Bernard et al) is directed to locating ROIs containing microcalcifications and enhancing the ROIs.
Applicants believe that the efficiency and effectiveness of image interpretation could be increased using a computer system by automatically detecting and then automatically enhancing the visualization of potential abnormalities in the identified ROIs. The improved display of a radiographic image with the enhanced visualization of these computer-detected ROIs may reduce the amount of time or even eliminate that time required for manual manipulation. The display with the enhanced ROIs can allow radiologists to assess these enhanced regions of interest in the context of overall image, which is less disruptive than the current method.
Particularly in mammography, various techniques and methods have been developed to enhance the contrast (both global and local) to help radiologists to better visualize the subtle difference in density between abnormalities and normal tissue. However, these methods are for general optimization purpose, not for a disease specific optimization purpose.
The present invention is directed to a method consisting of detecting ROIs containing a disease, using information such as computer-extracted features, disease type, patient information to select an optimal image processing to enhance individual ROIs for contrast enhancement and/or noise removal, enhancing individual ROIs with selected optimal processing methods corresponding to each ROI or enhancing the entire image with the selected optimal method.
The present invention will locally optimize ROIs or globally optimize the entire images based on computer-detected results. Further, ROIs are enhanced differently to highlight specific features interested based on computer extracted information and individual users' preferences. The customized enhancement will reduce or eliminate the amount of time spent on manual manipulation, thus improve the overall workflow with computer-aided detection.